Radiology

Trends in brain MRI and CP association using deep learning.

Cerebral palsy (CP) is a neurological disorder that dissipates body posture and impairs motor functions. It may lead to an intellectual disability and affect the quality of life. Early intervention is critical and challenging due to the uncooperative body movements of children, potential infant recovery, a lack of a single vision modality, and no specific contrast or slice-range selection and asso...

Role of Artificial intelligence model in prediction of low back pain using T2 weighted MRI of Lumbar spine.

BACKGROUND: Low back pain (LBP), the primary cause of disability, is the most common musculoskeletal disorder globally and the primary cause of disability. Magnetic resonance imaging (MRI) studies are inconclusive and less sensitive for identifying and classifying patients with LBP. Hence, this study aimed to investigate the role of artificial intelligence (AI) models in the prediction of LBP usin...

Dual Energy CT for Deep Learning-Based Segmentation and Volumetric Estimation of Early Ischemic Infarcts.

Ischemic changes are not visible on non-contrast head CT until several hours after infarction, though deep convolutional neural networks have shown pr...

Implementation of an AI Algorithm in Clinical Practice to Reduce Missed Incidental Pulmonary Embolisms on Chest CT and Its Impact on Short-Term Survival.

OBJECTIVES: A substantial number of incidental pulmonary embolisms (iPEs) in computed tomography scans are missed by radiologists in their daily routi...

Acceptability of artificial intelligence in dental radiology among patients in India: are we ready for this revolution?

OBJECTIVE: In recent times, artificial Intelligence (AI) has gained popularity in medical as well as dental radiology. Studies have been conducted amo...

A systematic scoping review exploring variation in practice in specimen mammography for Intraoperative Margin Analysis in Breast Conserving Surgery and the role of artificial intelligence in optimising diagnostic accuracy.

PURPOSE: Specimen Mammography (SM) is commonly used in Breast Conserving Surgery (BCS) for intraoperative margin analysis. A systematic scoping review...

Federated learning and deep learning framework for MRI image and speech signal-based multi-modal depression detection.

Adolescence is a significant period for developing skills and knowledge and learning about managing relationships and emotions by gathering attributes...

Development of HepatIA: A computed tomography annotation platform and database for artificial intelligence training in hepatocellular carcinoma detection at a Brazilian tertiary teaching hospital.

BACKGROUND: Hepatocellular carcinoma (HCC) is a prevalent tumor with high mortality rates. Computed tomography (CT) is crucial in the non-invasive dia...

An intuitive guidewire control mechanism for robotic intervention.

PURPOSE: Teleoperated Interventional Robotic systems (TIRs) are developed to reduce radiation exposure and physical stress of the physicians and enhan...

The Use of Remote Presence Robotic Tele-Presentation in Rural and Remote Canada: A Systematic Review.

One potential solution to limited health care in rural and remote regions is remote presence robotic tele-presentation to allow health care providers...

Artificial intelligence-enhanced detection of subclinical coronary artery disease in athletes: diagnostic performance and limitations.

PURPOSE: This study evaluates the diagnostic performance of artificial intelligence (AI)-based coronary computed tomography angiography (CCTA) for det...

Prediction of pathological complete response to chemotherapy for breast cancer using deep neural network with uncertainty quantification.

BACKGROUND: The I-SPY 2 trial is a national-wide, multi-institutional clinical trial designed to evaluate multiple new therapeutic drugs for high-risk...

Artificial intelligence for detection and characterization of focal hepatic lesions: a review.

Focal liver lesions (FLL) are common incidental findings in abdominal imaging. While the majority of FLLs are benign and asymptomatic, some can be mal...

Machine learning based classification of spontaneous intracranial hemorrhages using radiomics features.

PURPOSE: To assess the efficacy of radiomics features extracted from non-contrast computed tomography (NCCT) scans in differentiating multiple etiolog...

Exploring the integration of artificial intelligence in radiology education: A scoping review.

BACKGROUND: The integration of Artificial Intelligence (AI) into radiology education presents a transformative opportunity to enhance learning and pra...

Total-Body PET/CT: A Role of Artificial Intelligence?

The purpose of this paper is to provide an overview of the cutting-edge applications of artificial intelligence (AI) technology in total-body positron...

A survey on cell nuclei instance segmentation and classification: Leveraging context and attention.

Nuclear-derived morphological features and biomarkers provide relevant insights regarding the tumour microenvironment, while also allowing diagnosis a...

MixUNETR: A U-shaped network based on W-MSA and depth-wise convolution with channel and spatial interactions for zonal prostate segmentation in MRI.

Magnetic resonance imaging (MRI) plays a pivotal role in diagnosing and staging prostate cancer. Precise delineation of the peripheral zone (PZ) and t...

A deep learning approach for non-invasive Alzheimer's monitoring using microwave radar data.

Over 50 million people globally suffer from Alzheimer's disease (AD), emphasizing the need for efficient, early diagnostic tools. Traditional methods ...

Computed Tomography-Based Radiomics with Machine Learning Outperforms Radiologist Assessment in Estimating Colorectal Liver Metastases Pathologic Response After Chemotherapy.

OBJECTIVES: This study was designed to assess computed tomography (CT)-based radiomics of colorectal liver metastases (CRLM), extracted from posttreat...